Applications of Image Processing for Grading Agriculture products

Image processing in the context of Computer vision, is one of the renowned topic of computer science and engineering, which has played a vital role in automation. It has eased in revealing unknown fact in medical science, remote sensing, and many other domains. Digital image processing along with classification and neural network algorithms has enabled grading of various things. One of prominent area of its application is classification of agriculture products and especially grading of seed or cereals and its cultivars. Grading and sorting system allows maintaining the consistency, uniformity and depletion of time. This paper highlights various methods used for grading various agriculture products. DOI: 10.17762/ijritcc2321-8169.15036

ADVANCEMENT IN HARVESTING, PRE- COOLING AND GRADING OF FRUITS.

Generally, quality grading includes outer parameters (size, color intensity, color homogeneity, bruises, shape, stem identification surface texture and mass), inner parameters (sweetness, acidity or inner diseases) and freshness. All horticultural crops are high in water content and are subjected to desiccation and to mechanical injury. That is why these perishable commodities need very careful handling at every stage so that deterioration of produce is restricted as much as possible during the period between harvest and consumption.  Horticultural maturity is the stage of development when plant and plant part possesses the pre- requisites for use by consumers for a particular purpose i.e, ready to harvest. Post harvest handling is the final stage in the process of producing high quality fresh produce. Being able to maintain a level of freshness from the field to the dinner table presents many challenges. A grower who can meet these challenges will be able to expand his or her marketing opportunities and be better able to compete in the market place. Â

Grader: A review of different methods of grading for fruits and vegetables

Grading of agricultural produce especially the fruits and vegetables has become a perquisite of trading across borders.  In India mostly fruit growers grade the fruit manually.  Manual grading was carried out by trained operators who considered a number of grading factors and fruit were separated according to their physical quality.  Manually grading was costly and grading operation was affected due to shortage of labor in peak seasons.  Human operations may be inconsistent, less efficient and time consuming.  New trends in marketing as specified by World Trade Organization (WTO) demand high quality graded products.  Farmers are looking forward to having an appropriate agricultural produce-grading machine in order to alleviate the labor shortage, save time and improve graded product’s quality.  Grading of fruits is a very important operation as it fetches high price to the grower and improves packaging, handling and brings an overall improvement in marketing system.  The fruits are generally graded on basis of size and graded fruits are more welcome in export market.  Grading could reduce handling losses during transportation. Grading based on size consists of divergent roller type principle having inclination, expanding pitch type, inclined vibrating plate and counter rotating roller having inclination type graders.  Weight grading based on density and specific gravity of agricultural commodities.  The need to be responsive to market demand places a greater emphasis on quality assessment, resulting in the greater need for improved and more accurate grading and sorting practices.  Size variation in vegetables like potatoes, onions provided a base for grading them in different categories.  Every vegetable producing country had made their own standards of different grades keeping in view the market requirements.   Keywords: grading, handling, packaging, color sensor, specific gravity, Indi

Importance of Machine Vision Framework with Nondestructive Approach for Fruit Classification and Grading: A Review

Machine vision technology has gained significant importance in the agricultural industry, particularly in the non-destructive classification and grading of fruits. This paper presents a comprehensive review of the existing literature, highlighting the crucial role of machine vision in automating the fruit quality assessment process. The study encompasses various aspects, including image acquisition techniques, feature extraction methods, and classification algorithms. The analysis reveals the substantial progress made in the field, such as developing sophisticated hardware and software solutions, which have improved accuracy and efficiency in fruit grading. Furthermore, it discusses the challenges and limitations, such as dealing with variability in fruit appearance, handling different fruit types, and real-time processing. The identification of future research needs emphasizes the potential for enhancing machine vision frameworks through the integration of advanced technologies like deep learning and artificial intelligence.Additionally, it underscores the importance of addressing the specific needs of different fruit varieties and exploring the applicability of machine vision in real-world scenarios, such as fruit packaging and logistics. This review underscores the critical role of machine vision in non-destructive fruit classification and grading, with numerous opportunities for further research and innovation. As the agricultural industry continues to evolve, integrating machine vision technologies will be instrumental in improving fruit quality assessment, reducing food waste, and enhancing the overall efficiency of fruit processing and distribution

Quality grading of soybean seeds using image analysis

Image processing and machine learning technique are modified to use the quality grading of soybean seeds. Due to quality grading is a very important process for the soybean industry and soybean farmers. There are still some critical problems that need to be overcome. Therefore, the key contributions of this paper are first, a method to eliminate shadow noise for segment soybean seeds of high quality. Second, a novel approach for color feature which robust for illumination changes to reduces problem of color difference. Third, an approach to discover a set of feature and to form classifier model to strengthen the discrimination power for soybean classification. This study used background subtraction to reduce shadow appearing in the captured image and proposed a method to extract color feature based on robustness for illumination changes which was H components in HSI model. We proposed classifier model using combination of the color histogram of H components in HSI model and GLCM statistics to represent the color and texture features to strengthen the discrimination power of soybean grading and to solve shape variance in each soybean seeds class. SVM classifiers are generated to identify normal seeds, purple seeds, green seeds, wrinkled seeds, and other seed types. We conducted experiments on a dataset composed of 1,320 soybean seeds and 6,600 seed images with varies in brightness levels. The experimental results achieved accuracies of 99.2%, 97.9%, 100%, 100%, 98.1%, and 100% for overall seeds, normal seeds, purple seeds, green seeds, wrinkled seeds, and other seeds, respectivel

COLOR DEGRADATION KINETICS OF REHYDRATED ‘BORLOTTO’ BEANS STORED IN DIFFERENT GAS ATMOSPHERES AS MEASURED BY IMAGE ANALYSIS

Rehydrated legume seeds represent an important ingredient for ready-to-cook fresh soups packaged in a modified atmosphere. The aim of this work was to define an image analysis system for the evaluation of ‘Borlotto’ bean color changes during storage in different gas compositions. ‘Borlotto’ bean seeds were rehydrated in water for 12 hours and stored at 5 °C in controlled atmosphere using 4 different gas compositions: 3 % O2, 10 % CO2 in air, 3 % O2 + 10 % CO2, and air as control. An algorithm in Matlab was codified to measure color of the seed red stripes and ground color (L*, a*,b*, Hue angle and Chromaticity). Sensorial analyses based on the hedonic scale (from 5=excellent to 1=very bad) of the seed appearance were also carried out. Seed color and appearance changes over time were monitored initially and after 5, 10 and 15 days of storage at 5 °C. The obtained data were used to model the quality of degradation attributes over time, that were fit into first order kinetics. A gas composition with 3 % O2 + 10 % CO2 induced the least modification on the seed ground color, which received a highest appearance evaluation up to 10 days of storage, also showing that visual appearance changes were mostly affected by the variation of the seed ground color

Determination of Sprout-Damaged Barley Using Thermal Imaging

Pre-harvest sprouting is a major problem associated with cereal grains which results in lowering of end use quality. Pre-harvest sprouting affects the malting quality of barley.  The common methods to determine sprout damage are falling number, stirring number and amylograph peak viscosity, but these methods are time consuming.  There are other methods such as near infrared hyperspectral imaging and soft-x ray analysis which are still in the research stage.  Infrared thermal imaging technique to detect sprout damage is based on determining the changes in surface temperature distribution of grain which depends on the heat emission.  An infrared thermal camera was used in this study to determine whether sprout-damaged barley could be detected from healthy barley.  The results were analyzed using statistical and artificial neural network classifiers.  The classification accuracies were 78.7%, 78.9% and 88.5% for healthy; and 87.0%, 87.5% and 87% for sprouted kernels, using linear discriminant analysis, quadratic discriminant analysis and artificial neural network, respectively.  The results of the study show that thermal imaging has potential to determine sprout damage to barley.Keywords: grain, barley, sprout-damaged, thermal imaging, classification, Canad